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Design,methodology,and baseline of whole city‑million scale children and adolescents myopia survey(CAMS)in Wenzhou,China

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Eye and Vision 2021年第1期8卷 303-316页

作者： Liangde Xu Youyuan Zhuang Guosi Zhang Yunlong Ma Jian Yuan Changseng Tu MiaoMiao Li Wencan Wang Yaru Zhang Xiaoyan Lu Jing Li Xinting Liu Zhengbo Xue Meng Zhou Jie Sun Jinhua Bao Ming Li Fan Lu Hong Wang Jianzhong Su Jia Qu School of Ophthalmology and Optometry and Eye Hospital Wenzhou Medical UniversityWenzhou 325027China State Key Laboratory of Ophthalmology Optometry and Visual ScienceWenzhou 325027China Institute of Biomedical Big Data Wenzhou Medical UniversityWenzhou 325027China National Clinical Research Center for Ocular Disease Wenzhou 325027China College of Bioinformatics Science and Technology Harbin Medical UniversityHarbin 150081People’s Republic of China

Background:Myopia is the most common visual impairment in children and adolescents *** study described an economical and effective population-based screening pipeline and performed the project of a million scale children and adolescents myopia survey(CAMS),which will shed light on the further study of myopia from the level of epidemiology and precision ***:We developed a novel population-based screening pattern,an intelligent screening process and internetbased information transmission and analysis system to carry out the survey consisting of school children in Wenzhou,*** examination items include unaided distance visual acuity,presenting distance visual acuity,and noncycloplegic *** and high myopia were defined as spherical equivalent(SE)≤−1.00 diopters(D)and SE≤−6.00 D,***,the reports of the vision checking were automatically sent to parents and the related *** CAMS project will be done two to four times annually with the support of the *** online eyesight status information management system(OESIMS)was developed to construct comprehensive and efficient electronic vision health records(EVHRs)for myopia information inquiry,risk pre-warning,and further ***:The CAMS completed the first-round of screening within 30 days for 99.41%of Wenzhou students from districts and counties,in June 2019.A total of 1,060,925 participants were eligible for CAMS and 1,054,251(99.37%participation rate)were selected through data quality control,which comprised 1305 schools,and 580,609,251,050 and 170,967 elementary,middle,and high school *** mean age of participants was 12.21±3.32 years(6-20 years),the female-to-male ratio was *** prevalence of myopia in elementary,middle,and high school students was 38.16%,77.52%,and 84.00%,respectively,and the high myopia incidence was 0.95%,6.90%,and 12.98%.Conclusions:The CAMS standardized myopia screening model involves automating large-scal

关键词： Baseline Vision screening Myopia prevention and control Population-based

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Web Development of Direct-to-consumer Genetics Testing 4

Web Development of Direct-to-consumer Genetics Testing

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Azizatikarna, Kians Parung, Deby Erina Amirullah, Dian Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Budiarto, Arif Simon Pardamean, Bens Genetics Indonesia Jakarta12940 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

As the cost of genetic testing becomes more affordable each year, direct-to-customer (DTC) genetic testing services witness rapid market growth. This has encouraged the development of an easy-to-use website application to optimize potential customers to obtain informed choice regarding the offered DTC genetic testing as well as purchasing information. We present a wireframing process as a part of the agile software development process to build a web portal prototype for an Indonesia-based genetic testing service called DNAku. The approach in building the prototype used in this case is a part of the Agile Software Development Method. In brief, the wireframing stage resulted in this work consists of three main important outcomes. The first one is the use case diagram as a blueprint of this web-based system. A site map is then proposed as an extended version of the use case diagram which visualizes the whole pages with the links. Finally, based on this site map a series of page mock-ups are designed using Hyper Text Marking Language (HTML) and Cascading Style Sheets (CSS). The result of the prototype can be used as the basis for the next development stage, which is the coding stage based on the collected feedback from users. Considering the continuous application development via the wireframing method by taking into account the cycle of feedbacks from involved parties, this approach can strengthen the infrastructure to sustain DTC genetic business model, which has a niche market in Indonesia. © 2022 IEEE.

关键词： Sales

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Inhibition of ALDH2 by disulfiram leads to synthetic lethality via ROS strikes twice in ARID1A-deficient cholangiocarcinoma

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Genes & Diseases 2023年第1期10卷 69-71页

作者： Tingming Liang Lin Jia Rui Duan Lulu Shen Dekang Ren Yujie Ren Yuyang Dou Li Guo Jiangsu Key Laboratory for Molecular and Medical Biotechnology School of Life ScienceNanjing Normal UniversityNanjingJiangsu210023China Department of Bioinformatics Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu ProvinceSchool of Geographic and Biologic InformationNanjing University of Posts and TelecommunicationsNanjingJiangsu 210023China

Synthetic lethality describes an interaction whereby the co-occurrence of two mutations leads to cell death but one mutation alone does not,which can be exploited for cancer therapeutics.1 Due to lacking effective nonsurgical treatment and early clinical diagnosis markers,patients have high mortality and low overall survival rates in cholangiocarcinoma(CCA).

关键词： diagnosis mortality alone

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Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER

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Nature Biotechnology 2025年 1-13页

作者： Zheng, Wei Wuyun, Qiqige Li, Yang Liu, Quancheng Zhou, Xiaogen Peng, Chunxiang Zhu, Yiheng Freddolino, Lydia Zhang, Yang NITFID School of Statistics and Data Science AAIS LPMC and KLMDASR Nankai University Tianjin China Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Department of Computer Science and Engineering Michigan State University East LansingMI United States Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Department of Biological Chemistry University of Michigan Ann ArborMI United States Department of Computer Science School of Computing National University of Singapore Singapore Singapore Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore

The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly refinement (D-I-TASSER), which constructs atomic-level protein structural models by integrating multisource deep learning potentials with iterative threading fragment assembly simulations. D-I-TASSER introduces a domain splitting and assembly protocol for the automated modeling of large multidomain protein structures. Benchmark tests and the most recent critical assessment of protein structure prediction, 15 experiments demonstrate that D-I-TASSER outperforms AlphaFold2 and AlphaFold3 on both single-domain and multidomain proteins. Large-scale folding experiments further show that D-I-TASSER could fold 81% of protein domains and 73% of full-chain sequences in the human proteome with results highly complementary to recently released models by AlphaFold2. These results highlight a new avenue to integrate deep learning with classical physics-based folding simulations for high-accuracy protein structure and function predictions that are usable in genome-wide applications. © The Author(s) 2025.

关键词： Deep learning

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M6A peak calling accounting for varying sequencing bias across regions and samples

M6A peak calling accounting for varying sequencing bias acro...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Lanyu Guo, Zhenxing Qin, Zhaohui Wu, Zhijin Brown University Dept of Biostatistics ProvidenceRI02903 United States The Chinese University of Hong Kong School of Data Science Shenzhen China Emory University Dept of Biostatistics and Bioinformatics AtlantaGA30329 United States

ISBN: (纸本)9798350386226

N6-Methyladenosine (m6A) is the most abundant type of mRNA methylation and is most widely measured by methylated RNA immunoprecipitation sequencing (MeRIP-seq). In MeRIP-seq, an immunoprecipitation (IP) sample and a pairing control (input) sample are sequenced for each biological sample. Methylated regions are identified as peaks showing increased counts in the IP sample versus the input. We report that technical bias in sequencing can vary substantially in the IP and input samples depending on the local sequence context. Current sequencing depth-based normalization does not appropriately account for the varying technical bias along the transcriptome and leads to inaccurate identification of m6A regions. We describe a method to estimate a local size factor that reflects the RNA sequence context and show that peak calling using these region-specific size factors identifies more accurate peak regions. © 2024 IEEE.

关键词： Methylation

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On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization

arXiv

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arXiv 2021年

作者： Luo, Yuetian Li, Xudong Zhang, Anru R. Data Science Institute University of Chicago United States School of Data Science Fudan University China Department of Biostatistics and Bioinformatics Duke University United States

In this paper, we propose a general procedure for establishing the geometric landscape connections of a Riemannian optimization problem under the embedded and quotient geometries. By applying the general procedure to the fixed-rank positive semidefinite (PSD) and general matrix optimization, we establish an exact Riemannian gradient connection under two geometries at every point on the manifold and sandwich inequalities between the spectra of Riemannian Hessians at Riemannian first-order stationary points (FOSPs). These results immediately imply an equivalence on the sets of Riemannian FOSPs, Riemannian second-order stationary points (SOSPs), and strict saddles of fixed-rank matrix optimization under the embedded and the quotient geometries. To the best of our knowledge, this is the first geometric landscape connection between the embedded and the quotient geometries for fixed-rank matrix optimization and it provides a concrete example of how these two geometries are connected in Riemannian optimization. In addition, the effects of the Riemannian metric and quotient structure on the landscape connection are discussed. We also observe an algorithmic connection between two geometries with some specific Riemannian metrics in fixed-rank matrix optimization: there is an equivalence between gradient flows under two geometries with shared spectra of Riemannian Hessians. A number of novel ideas and technical ingredients including a unified treatment for different Riemannian metrics, novel metrics for the Stiefel manifold, and new horizontal space representations under quotient geometries are developed to obtain our results. The results in this paper deepen our understanding of geometric and algorithmic connections of Riemannian optimization under different Riemannian geometries and provide a few new theoretical insights to unanswered questions in the literature. © 2021, CC BY-SA.

关键词： Geometry

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Deep Learning for Imbalance data Classification using Class Expert Generative Adversarial Network

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Procedia Computer science 2018年 135卷 60-67页

作者： Fanny Tjeng Wawan Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification.

关键词： imbalance data classification deep learning generative adversarial network

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Twitter-based text classification using svm for weather information system 6

Twitter-based text classification using svm for weather info...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Purwandari, Kartika Rahutomo, Reza Sigalingging, Join W. C. Ajy Kusuma, Mahisa Prasetyo, Aji Pardamean, Bens Research Center Bina Nusantara University Bioinformatics and Data Science Jakarta11480 Indonesia BMKG Meteorological Climatological and Geophysical Agency Database Center Division Jakarta10720 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

The popularity of social media amplified the amount of text data that is used to enrich text classification research with machine learning approach. The modernization in weather information system is needed to produce real-time weather information using the involvement of Twitter users. GetOldTweets was used to cover data acquisition from Twitter. Support Vector Machine (SVM) was deployed as a machine learning model for text classification. The result was delivered in Esri Maps features. It facilitated a social sensing for Indonesia weather information system by using Twitter posts from Indonesian users with 93% accuracy. © 2021 IEEE.

关键词： data acquisition

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A Fast and Accurate Model of Thoracic Disease Detection by Integrating Attention Mechanism to a Lightweight Convolutional Neural Network

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Procedia Computer science 2021年 179卷 112-118页

作者： Branden Adam Sangeroki Tjeng Wawan Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

The utilization of Deep Learning, especially the Convolutional Neural Network (CNN), is currently the best approach for thoracic disease detection from Chest X-Ray images. However, CNN typically has a slow runtime, hence potentially can introduce a bottleneck in the healthcare that uses the technology. To answer the challenge, we proposed a model that integrates an attention mechanism to a lightweight CNN. The proposed model can run faster than the state-of-the-art thoracic disease detection model while having the second-best performance among the thoracic disease detection models.

关键词： chest x-ray thoracic disease deep learning attention mechanism convolutional neural network

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Feature Pyramid Networks for Crowd Counting

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Procedia Computer science 2019年 157卷 175-182页

作者： Tjeng Wawan Cenggoro Ayu Hidayah Aslamiah Ardian Yunanto Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

In this paper, we propose the use of Feature Pyramid Networks (FPN) for Crowd Counting problem. FPN previously has been used for retinanet, the state-of-the-art model for object detection. By using FPN, our proposed crowd counting model achieved a state-of-the-art performance for UCF CC 50 dataset with MAE 136.4 and MSE 223.6. The proposed model is also achieved a state-of-the-art MSE value of 7.6 for ShanghaiTech Part B dataset. The code can be accessed at https: //***/wawancenggoro/fpncc.

关键词： Crowd Counting Scale-Aware Deep Learning Feature Pyramid Networks

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